I begin the analysis by reading in the data with DESeq and running DESeq() on the data. After which, I am left with a DESeq object for downstream analysis. First, I will run PCA by running VarianceStabilizingTransformation and PCA on the top 500 genes. The most striking difference lies along PC1 (x-axis) and seems to be due to FB vs Br. There also seem to be differences between Male and Female FB samples, while Female and Male BR samples are more similar.

Next I will run a culstered heatmap of sample distances to determine the similarity between samples. Dark blue colors indicate samples that are more similar, while lighter blue colors indicate samples that are more dissimilar.

MaleFB vs FemaleFB

Lets begin to dig into the comparisons. First we will look at MaleFB vs. FemaleFB.

Gene Ontology Next, we will perform gene ontology/pathway analysis for genes with higher expression in MaleFB relative to FemaleFB

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Finally, we will perform gene ontology/pathway analysis for genes with higher expression in FemaleFB relative to MaleFB

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#Heatmap of top 100

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